The Netherlands is a frontrunner in the field of public charging infrastructure, having a high number of public charging stations per electric vehicle (EV) in the world. During the early years of adoption (2012-2015) a large percentage of the EV fleet were Plugin Hybrid Electric Vehicles (PHEV)due to the subsidy scheme at that time. With an increasing number of Full Electric Vehicles (FEVs) on the market and a current subsidy scheme for FEV only, a transition of the EV fleet from PHEV to FEV is expected. This is hypothesized to have effect on charging behavior of the complete fleet, reason to understand better how PHEVs and FEVs differ in charging behavior and how this impacts charging infrastructure usage. In this paper, the effects of the transition of PHEV to FEV is simulated by extending an existing Agent Based Model. Results show important effects of this transitionon charging infrastructure performance.
With the rise of the number of electric vehicles, the installment of public charging infrastructure is becoming more prominent. In urban areas in which EV users rely on on-street parking facilities, the demand for public charging stations is high. Cities take on the role of implementing public charging infrastructure and are looking for efficient roll-out strategies. Municipalities generally reserve the parking spots next to charging stations to ensure their availability. Underutilization of these charging stations leads to increased parking pressure, especially during peak hours. The city of The Hague has therefore implemented daytime reservation of parking spots next to charging stations. These parking spots are exclusively available between 10:00 and 19:00 for electric vehicles and are accessible for other vehicles beyond these times. This paper uses a large dataset with information on nearly 40.000 charging sessions to analyze the implementation of the abovementioned scheme. An unique natural experiment was created in which charging stations within areas of similar parking pressure did or did not have this scheme implemented. Results show that implemented daytime charging 10-19 can restrict EV owners in using the charging station at times when they need it. An extension of daytime charging to 10:00-22:00 proves to reduce the hurdle for EV drivers as only 3% of charging sessions take place beyond this time. The policy still has the potential to relieve parking pressure. The paper contributes to the knowledge of innovative measures to stimulate the optimized rollout and usage of charging infrastructure.
The mass adoption of Electric Vehicles (EVs) might raise pressure on the power system, especially during peak hours. Therefore, there is a need for delayed charging. However, to optimize the charging system, the progression of charging from an empty battery until a full battery of the EVs based on realworld data needs to be analyzed. Many researchers currently view this charging profile as a static load and ignore the actual charging behavior during the charging session. This study investigates how different factors influence the charging profile of individual EVs based on real-world data of charging sessionsin the Netherlands, enabling optimization analysis of EV smart charging schemes.
